What is AI?
AI, also known as Artificial intelligence, is a technology with human-like problem-solving capabilities. AI in action appears to simulate human intelligence—it can recognize images, write poems, and make data-based predictions.
Modern organizations collect large data volumes from diverse sources, such as smart sensors, human-generated content, monitoring tools, and system logs. Artificial intelligence technologies analyze the data and use it to assist business operations effectively. For example, AI technology can respond to human conversations in customer support, create original images and text for marketing, and make smart suggestions for analytics.
Ultimately, artificial intelligence is about making software smarter for customized user interactions and complex problem-solving.
What are some types of AI technologies?
AI apps and technologies have increased exponentially in the last few years. Below are some examples of common AI technologies you may have encountered.
History of AI
In his 1950 paper, "Computing Machinery and Intelligence," Alan Turing considered whether machines could think. In this paper, Turing first coined the term artificial intelligence and presented it as a theoretical and philosophical concept. However, AI, as we know it today, is the result of the collective effort of many scientists and engineers over several decades.
1940-1980
In 1943, Warren McCulloch and Walter Pitts proposed a model of artificial neurons, laying the foundation for neural networks, the core technology within AI.
Quickly following, in 1950, Alan Turing published "Computing Machinery and Intelligence," introducing the concept of the Turing Test to assess machine intelligence.
This lead to graduate students Marvin Minsky and Dean Edmonds building the first neural net machine known as the SNARC, Frank Rosenblatt developed the Perceptron which is one of the earliest models of a neural network, and Joseph Weizenbaum created ELIZA, one of the first chatbots to simulate a Rogerian psychotherapist between 1951 and 1969.
From 1969 until 1979 Marvin Minsky demonstrated the limitations of neural networks, which caused a temporary decline in neural network research. The first "AI winter" occurred due to reduced funding and hardware and computing limitations.
1980-2006
In the 1980's, there was a renewed interest and government funding for AI research primarily in translation and transcription.During this time, expert systems, like MYCIN, became popular because they simulated human decision-making processes in specific domains like medicine. With the 1980's revival of neural networks, David Rumelhart and John Hopfield published papers on deep learning techniques showing that computers could learn from experience
From 1987-1997, due to other socio-economic factors and the dot-com boom, a second AI winter emerged. AI research became more fragmented, with teams solving domain-specific problems across different use cases.
Starting in 1997 to about 2006, we saw significant ahcievements in AI including IBM's Deep Blue chess software defeated world chess champion Garry Kasparov. In addition to this Judea Pearl published a book that included probability and decision theory in AI research and Geoffrey Hinton and others popularized deep learning, leading to a resurgence in neural networks. However, commercial interest remained limited.
2007-Present
From 2007 to 2018, advancement in cloud computing made computing power and AI infrastructure more accessible. It led to increasing adoption. innovation and advancement in machine learning. The advancements included a convolutional neural network (CNN) architecture called AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton winning the ImageNet competition, showcasing the power of deep learning in image recognition and Google's AlphaZero mastered the games of chess, shogi, and Go without human data, relying on self-play.
In 2022, chatbots that uses artificial intelligence (AI) and natural language processing (NLP) to have human-like conversations and complete tasks like OpenAI's ChatGPT became widely known for its conversational abilities, renewing AI interest and development.
AI in the future
Current artificial intelligence technologies all function within a set of pre-determined parameters. For example, AI models trained in image recognition and generation cannot build websites.
Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to perform tasks for which it is not necessarily trained or developed.
AGI is a theoretical pursuit to develop AI systems with autonomous self-control, reasonable self-understanding, and the ability to learn new skills. It can solve complex problems in settings and contexts that were not taught at its creation. AGI with human abilities remains a theoretical concept and research goal. It is one possible future of AI.
